Niao He: Catalogue data in Autumn Semester 2023 |
| Name | Prof. Dr. Niao He |
| Field | Computer Science |
| Address | Professur für Informatik ETH Zürich, OAT Y 21.1 Andreasstrasse 5 8092 Zürich SWITZERLAND |
| niao.he@inf.ethz.ch | |
| URL | https://odi.inf.ethz.ch/ |
| Department | Computer Science |
| Relationship | Assistant Professor (Tenure Track) |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 252-0945-17L | Doctoral Seminar Machine Learning (HS23)  Only for Computer Science Ph.D. students. This doctoral seminar is intended for PhD students affiliated with the Institute for Machine Learning. Other PhD students who work on machine learning projects or related topics need approval by at least one of the organizers to register for the seminar. | 2 credits | 1S | N. He, V. Boeva, J. M. Buhmann, R. Cotterell, T. Hofmann, A. Krause, G. Rätsch, M. Sachan, J. Vogt, F. Yang | |
| Abstract | An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills. | ||||
| Learning objective | The seminar participants should learn how to prepare and deliver scientific talks as well as to deal with technical questions. Participants are also expected to actively contribute to discussions during presentations by others, thus learning and practicing critical thinking skills. | ||||
| Prerequisites / Notice | This doctoral seminar of the Machine Learning Laboratory of ETH is intended for PhD students who work on a machine learning project, i.e., for the PhD students of the ML lab. | ||||
| 252-5051-00L | Advanced Topics in Machine Learning The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | 2 credits | 2S | J. M. Buhmann, R. Cotterell, M. El-Assady, N. He, F. Yang | |
| Abstract | In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning. | ||||
| Learning objective | The seminar "Advanced Topics in Machine Learning" familiarizes students with recent developments in pattern recognition and machine learning. Original articles have to be presented and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth while omitting details which are not essential for the understanding of the work. The presentation style will play an important role and should reach the level of professional scientific presentations. | ||||
| Content | The seminar will cover a number of recent papers which have emerged as important contributions to the pattern recognition and machine learning literature. The topics will vary from year to year but they are centered on methodological issues in machine learning like new learning algorithms, ensemble methods or new statistical models for machine learning applications. Frequently, papers are selected from computer vision or bioinformatics - two fields, which relies more and more on machine learning methodology and statistical models. | ||||
| Literature | The papers will be presented in the first session of the seminar. | ||||